Metacognitive Online Reading, Navigational Strategies, and the Reading Performance of the Grade 11 HUMMS of Pedro T. Mendiola Sr. Memorial National High School
Bibliographic record
Abstract
This predictive, cross-sectional study aimed to determine the metacognitive online reading and navigational strategies and their relation to the reading performance of Grade 11 HUMSS Students of Pedro T. Mendiola Sr. Memorial National High School. Furthermore, the study also investigated which factors of metacognitive online reading and navigational strategies significantly influence the respondents’ reading performance. One hundred twenty-five (125) students selected through simple random sampling participated in the study. Data were gathered using a Google Form and reading fluency test. Descriptive Statistics such as weighted mean, Pearson- Product correlation, and regression analysis were used to interpret the data. The students’ extent of the metacognitive online reading and navigational strategies is high, while the students’ reading performance is instructional. The metacognitive online reading strategy is strongly related to reading performance. The navigational strategy is moderately related to reading performance. All indicators of metacognitive online learning strategy significantly predict reading performance. Only mixed overview as an indicator of navigational strategy significantly predicts the reading performance. Senior High school students who used metacognitive online reading navigational strategies had definite reading goals in mind and knew how to accomplish them. Students need teacher support at the instructional reading performance level. The metacognitive and navigational strategies significantly predict and influence the respondents’ reading performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".